Microstructure/reports/methodology_limitations.md

Methodology and limitations

Historical calendar note (2026-08-23): Any Aug 8–11 live-L2 schedule in this document is superseded by the v2 Aug 10–13 protocol recorded in the README and docs/M8_L2_ANALYSIS_CONTRACT.md. It is retained as research history, not current campaign authority.

Evidence tiers

• SYNTHETIC_SMOKE verifies deterministic software behavior only. Its values are neither market observations nor investment evidence.
• PUBLIC_SAMPLE_PARTIAL describes a fixed, bounded public-data interval. It may support interval-specific research observations but not broad generalization.
• FULL_DATA is reserved for a manifested empirical study meeting its declared coverage and acceptance tests. It still represents research and simulation, not realized live performance.
• INSUFFICIENT_DATA is a terminal evidence status, not a lower-quality set of model estimates. It means a predeclared input or quality gate prevented the required evaluation; absent predictions and execution fields remain absent rather than being imputed, rerun on replacement dates, or reported as zeros.

Evidence tier is derived from data manifests. A synthetic source cannot be promoted by changing a report label.

Time and observability

All reported intervals are UTC. Stable event ordering uses the normalized event timestamp plus sequence and event identifiers; tied timestamps are not reordered arbitrarily. Exchange event time is not automatically equivalent to local receipt time. A feature at decision event t may contain only values observable at or before t; its label begins strictly after t. Decision and order latency are separate assumptions.

Time-ordered evaluation is necessary but not sufficient. When label intervals overlap a fold boundary, affected training rows must be purged. Configured embargo separates adjacent folds. The final test period is not used for model selection, feature selection, hyperparameter tuning, threshold choice, or probability calibration.

Data lineage and quality

External raw data is immutable and excluded from Git. Each download or fixture requires a source, retrieval time or deterministic generation rule, requested and observed period, schema version, row count, and checksum. Normalization and later exclusions create new artifacts rather than rewriting raw observations.

Validation covers duplicates, out-of-order timestamps, missing sequence ranges, crossed books, nonpositive price or quantity, abnormal spread, long silence, and clock discontinuities. A warning does not prove an observation is harmless. A fatal gap can require abandoning an affected reconstruction segment rather than interpolating it.

Public endpoints can change schema, retention, throttling, or geographic availability. Download success does not establish completeness. Exchange maintenance, symbol-rule changes, clock behavior, delistings, and missing markets can bias a selected sample.

The canonical full-archive trade M8 result illustrates this boundary. BTCUSDT training normalization completed on 2,071,461 rows with no findings. ETHUSDT training normalization completed on 987,297 rows with zero errors but 53 long- silence warnings, violating the frozen zero-warning gate. Selection did not start, neither held-out date was opened, and execution was not run. This supports the conclusion that the declared trade study was data-insufficient; it neither supports nor refutes the economic hypothesis. Relaxing the warning rule or choosing another date after seeing that terminal would invalidate the protocol.

Market-state and feature measurement

Order-flow imbalance, signed volume, intensity, spread, depth, queue imbalance, microprice, volatility, price impact, liquidity recovery, and regime features are conditional on the event types actually observed. Trade signing can be wrong. Displayed depth can be cancelled before execution. Aggregated or trade-only data cannot identify hidden orders, matching-engine priority, or individual queue position. Cancellation intensity is unavailable unless the feed exposes enough book history to measure it defensibly.

Feature windows create serial dependence, and overlapping future labels reduce effective sample size. Intraday and volatility regimes may be unbalanced. A relationship can reflect a common response to news rather than a causal effect of order flow on price.

Statistical modeling

Simple historical or majority baselines anchor the model ladder. Linear and regularized models provide interpretable comparisons; a tree model tests bounded nonlinearity. More complex time-series or point-process models require a stated economic reason and evidence that simpler models leave meaningful structure.

ROC-AUC alone can obscure calibration and class imbalance. Classification reports should include log loss, Brier score, precision-recall diagnostics where useful, class rates, and calibration. Regression reports require scale-aware errors and a baseline comparison. Bootstrap confidence intervals must respect temporal dependence. Repeated instruments, horizons, regimes, features, thresholds, and models create multiplicity; an unadjusted favorable result is exploratory.

Model importance is not structural causality. Feature rankings can be unstable under correlation or regime shift. A selected model may decay after the observed period, and cryptocurrency venue behavior may not transfer to equities, futures, or fragmented markets.

Execution and fills

Predictive metrics are not execution results. Simulated economics depend on maker and taker fees, half-spread and slippage, decision and order latency, order size, fill probability, queue proxy, partial fills, adverse selection, inventory cap, liquidation, and capacity assumptions. Each assumption must be serialized and sensitivity-tested.

A queue proxy is not true priority. A fill inferred from subsequent traded volume can be optimistic when cancellations, hidden liquidity, competing orders, and matching rules are unknown. Limit-order simulations can suffer severe adverse selection; market-order simulations can understate impact. Forced end-of-period liquidation may dominate a short sample. Capacity extrapolation from public top-of- book data is especially uncertain.

Annualized return or Sharpe-like statistics are inappropriate for synthetic or very short runs. Simulated P&L excludes operational failures, exchange outages, funding and financing where omitted, taxes, custody, counterparty risk, and live model drift. The project contains no order-entry path and is not a deployment system.

The frozen live-L2 extension narrows execution further. It permits market-order scenarios only, with threshold, reference notional/depth, lot rounding, L1 fill cap, inventory, liquidation, fee, and decision/order latency rules fixed before held-out access. Latencies in this campaign are event counts, not milliseconds. Recorded L1 limits the scenario fill and any residual is cancelled; no deeper walk, hidden liquidity, endogenous reaction, or true impact is modeled. The frozen zero-extra-slippage setting is one transparent scenario, not evidence that slippage is zero. Capacity, realized execution, limit-fill, queue-priority, and profitability claims remain forbidden.

Prospective live-L2 boundary

The Aug 8--11 BTCUSDT/ETHUSDT sessions must share one clean capture runtime identity and pass the exact simultaneous observed-interval gates. Raw websocket receipt time is available, but it is internet-path receipt time—not a colocated clock or matching-engine acknowledgment. A long nominal session cannot conceal gaps: features and labels are limited to verified observed intervals, and clock targets are censored if no sufficiently fresh same-interval state exists.

The campaign authority also binds an outcome-blind nonce, the one canonical output-root path and filesystem identity, the loaded package/module origin, and a hashed Python/platform/production-dependency fingerprint. These controls make environment and storage substitution visible, but they do not make public- internet latency colocated or prove that the exchange feed was complete.

Regime thresholds are fit on Aug 8 only. When both development sessions are complete, model selection and calibration use Aug 8/9 only and must be committed in eight symbol-by-endpoint child locks plus one aggregate LOCKED authority before Aug 10/11 frames are exposed. If either development session is insufficient, a control-only NOT_CREATED authority is committed instead and no economic frame from any session is opened. Held-out evaluation restores only a LOCKED state without refit. Paired moving-block intervals partially address serial dependence but do not prove independence, solve all overlapping-horizon dependence, correct every model/horizon/regime comparison, or authorize a p-value. Directional agreement across two adjacent one-hour sessions is still a narrow venue- and period-specific result.

At the pre-capture source freeze, these were software and governance controls, not book evidence: no declared L2 session bundle or downstream L2 metric had been promoted. This tracked file is intentionally unchanged during the four-day campaign. The exact field-level authority is docs/M8_L2_ANALYSIS_CONTRACT.md; any eventual numbers must come from a verified generated bundle rather than this source-controlled limitations file.

The completed final producer takes four explicit session path/manifest/checksum authorities plus the development-authority path and SHA. A development or held-out session failure or no eligible label produces a checksummed INSUFFICIENT_DATA terminal with no promoted evaluation or execution, rather than an opportunistic retry. A complete run copies exact control authorities into a self-contained snapshot but still revalidates their external originals. Reports are re-rendered from a checksummed report-input snapshot into a separate directory; report generation cannot mutate the immutable empirical bundle. These are integrity and governance guarantees, not proof that the economic design or market conclusion is correct.

Reporting and generalizability

Generated reports read serialized artifacts; they do not recalculate statistics. Every surface shows evidence tier, observed UTC interval, configuration hash, input-manifest hashes, Git commit or UNBORN, and dirty state. Missing values are N/A, never zero. Checksums demonstrate byte integrity, not correctness of the economic design.

Results from BTCUSDT and ETHUSDT on one venue cannot be assumed to apply to other symbols, venues, asset classes, tick sizes, participant mixes, or regulatory settings. Robustness requires predeclared adjacent periods, cross-instrument and regime comparisons, alternative defensible execution assumptions, and careful documentation of results that fail.